Iterative Learning Scheme Based Sensor Fault Estimation for Nonlinear Repetitive System

Li Feng,Shuiqing Xu,Meng Deng,Ke Zhang

doi:10.1109/access.2019.2927884

Abstract

Iterative learning scheme is proposed with the aim to achieve perfect tracking of a prescribed reference trajectory for systems that operate repetitively. In this paper, a sensor fault estimation framework is proposed for nonlinear repetitive system. First, the problem of sensor fault estimation is converted to actuator fault estimation via state redefinition. Afterward, state observer is designed for state reconstruction while iterative learning law is presented for fault estimation. Thus, the information in the previous period can be utilized to improve the fault estimating performance in current iterative trial. Avoiding the uncertainty caused by the norm optimal theory, the uniform convergence of error extended system is guaranteed by asymptotic stability and optimal function. Finally, the efficiency and merits of the proposed scheme are illustrated by numerical examples.

Highlights

With the growing demand for safety and reliability in industrial processes, considerable attention in fault tolerate control [1]–[3] have been witnessed in both academic and technical fields
Motived by the discussions above, this paper presents the design of an iterative learning scheme based sensor fault estimation for nonlinear systems by using stability and optimal theories
The proposed iterative learning scheme–based approach can be applicable to more general nonlinear repetitive systems

Summary

INTRODUCTION

With the growing demand for safety and reliability in industrial processes, considerable attention in fault tolerate control [1]–[3] have been witnessed in both academic and technical fields. L. Feng et al.: Iterative Learning Scheme-Based Sensor Fault Estimation for Nonlinear Repetitive System been a hot topic of existing researches. Motived by the discussions above, this paper presents the design of an iterative learning scheme based sensor fault estimation for nonlinear systems by using stability and optimal theories. (1) Compared with the existing observer-based results [7]–[9], the proposed method for nonlinear repetitive system both considered the stability of state estimation error system in time domain and the tracking error convergence in the iterative domain. (2) Different from the iterative learning methods [14]–[18] depended on the optimal theory, the Lyapunov function is presented to verify the stability of state estimation error system and the optimal function is designed to guarantee the iterative tracking error convergence. The rest of the paper is arranged as follows: Section II gives the problem formulation and ILC law for nonlinear system, Section III gives the detailed analysis of the convergence, Illustrative simulations are shown in Section IV and Section V concludes the paper

PROBLEM FORMULATION

SIMULATION EXAMPLES

CONCLUSION